Some Remarkable Education Statistics in Turkey

Atakar

Team Members

  1. Semih Atalay - 090180314
  2. Mahmud Turab Karakaş - 090180306
library(tidyverse)
library(readxl)
library(rvest)
library(sf)
library(leaflet)
library(ggplot2)
library(gganimate)
library(gridExtra)
library(tmaptools)
mytheme <- theme_set(theme_minimal()) 
mytheme <- theme_update(panel.grid = element_line(linetype = "solid", size = 0.4),
                        panel.grid.major.x = element_blank(), 
                        panel.grid.minor= element_blank(), 
                        plot.title = element_text(face="bold"), 
                        plot.caption = element_text(hjust = 0))

Project Goal

Education helps new generations acquire the necessary knowledge, skills and understanding and develop their personalities in order to take their place in social life.In addition, the greatest wealth for a nation is not its underground or above-ground wealth, but the main great wealth is the generations of quality individuals that it has raised thanks to the education of that country. Educational processes of individuals in Turkey end up in different education levels due to various reasons such as the geographical locations of individuals, the insensitivity of parents or students to education, family problems, economic inadequacies, lack of working environment for students at home, physical inadequacy of schools. Differences in completed education levels are reflected to individuals as the gap between income levels in their future business life. Therefore, the aim of this project is to share and evaluate some remarkable education statistics in Turkey.

Introduction of First Education Statistic

The first aim of the project is to see whether the level of education completed contributes to people’s income, and to observe whether the gender difference creates a privilege in the income earned for the same education levels. For this purpose, data showing annual average gross earnings by gender and completed education level for the years 2006, 2010, 2014 and 2018 were used.

data1 = read_excel("data/average_annual_gross_earnings_by_education_level.xls", range = "A8:F27")

data1 = data1[,-1]

colnames(data1)[c(1:5)] = paste(c("com_edu_lev",
                                  "2006",
                                  "2010",
                                  "2014",
                                  "2018"))
data1 = drop_na(data1)

i = 1
while (i <= 15 ) {
data1[c(i),1]   = "Primary school and below"
data1[c(i+1),1] = "Primary education and secondary school"
data1[c(i+2),1] = "High school"
data1[c(i+3),1] = "Vocational high school"
data1[c(i+4),1] = "Higher education"
i = i+5;
}

data1 = data1 %>%
  mutate(type = case_when(rownames(data1) %in% c(1:5) ~ "total",
                            rownames(data1) %in% c(6:10) ~ "male",
                            TRUE ~ "female"))

data1 = pivot_longer(data1, "2006":"2018", names_to = "Year")
com_edu_lev type Year value
Primary school and below total 2006 9676.323
Primary school and below total 2010 13099.221
Primary school and below total 2014 18602.383
Primary school and below total 2018 35171.056
Primary education and secondary school total 2006 9640.158
Primary education and secondary school total 2010 13043.379
data1 %>%
  filter(type == "total") %>%
  ggplot(aes(x=com_edu_lev, y=value, fill = Year)) +
  geom_bar(stat = "identity", position ="dodge") +
  scale_fill_manual(values=c("2006"= "#f20707","2010"="#ba0606","2014"="#800303","2018"="#5e0202")) +  
  labs(x="", y="",
       title = "What Is The Average Annual Gross Earnings by Completed Education Level?",
       subtitle = "Total") + 
  scale_y_continuous(expand=c(0,0),limits = c(0,78000), n.breaks = 20 )  +
   scale_x_discrete(
            breaks = c("Primary school and below",
                       "Primary education and secondary school",
                       "High school",
                       "Vocational high school",
                       "Higher education"),
            label = c("Primary",
                      "Secondary",
                      "High School",
                      "Vocational High",
                      "Higher Education"),
            limits = c("Primary school and below",
                       "Primary education and secondary school",
                       "High school",
                       "Vocational high school",
                       "Higher education")) +
  theme(axis.text.x= element_text(size=8,color="black"),
        axis.text.y =element_text(size=8,color="black"),
        plot.title=element_text(color="black",size=10),
        plot.subtitle = element_text(color="black",size=10),
        legend.title=element_text(color="black",size=9))

As can be seen from these three graphs, the annual average earnings of those who have completed primary, secondary and high school are almost the same, regardless of year and gender. It is seen that the earnings of the people who have completed the vocational high school are higher than the high school. As expected, the people with the highest earnings are college graduates.

data1 %>%
  filter(Year == 2006 & ( type == "female" | type == "male" )) %>%
  ggplot(aes(x=com_edu_lev, y=value, fill = type)) +
    geom_bar(stat="identity", position = "dodge") +
    scale_fill_manual(values=c("male"="#3197ad", "female"="#a80d7a")) +
  labs(x="", y="",
       title = "Comparison of Average Annual Gross Earnings of Men and Women \nby Completed Education Level",
       subtitle = "Year: 2006") +
  scale_y_continuous(expand=c(0,0),limits = c(0,33000), n.breaks = 20 )  +
  scale_x_discrete(
            breaks = c("Primary school and below",
                       "Primary education and secondary school",
                       "High school",
                       "Vocational high school",
                       "Higher education"),
            label = c("Primary",
                      "Secondary",
                      "High",
                      "Vocational",
                      "Higher"),
            limits = c("Primary school and below",
                       "Primary education and secondary school",
                       "High school",
                       "Vocational high school",
                       "Higher education")) +
  theme(axis.text.x = element_text(size=8,color="black"),
        axis.text.y =element_text(size=8,color="black"),
        plot.title=element_text(color="black",size=10),
        plot.subtitle = element_text(color="black",size=10),
        legend.title=element_blank())

When compare the annual average earnings as men and women, it would not be wrong to say that men with the same education level earn a little more than women with the same education level, regardless of the year.

Introduction of Second Education Statistic

The second aim of the project is to reveal the reasons why individuals who have completed an education level but have not completed their education until university cannot complete the education process and show how these reasons change due to gender difference. For this reason, data prepared by considering individuals between the ages of 15-34 in the second quarter of 2016 and showing the reasons why individuals did not complete their education until university were used.

data2 = read_excel("data/reasons_for_not_completing_university.xls", range = "A6:K23")

data2 = data2 %>%
          setNames(c("completed_education_level",
                     "Total",
                     "his/her education is enough",
                     "failed exam",
                     "not interested in school",
                     "to cost of studying to high",
                     "want to work",
                     "family or spouse not allow schooling",
                     "marriage or other family reason",
                     "disability and health reason",
                     "other")) %>%
          select(-2)


data2$`family or spouse not allow schooling`  = as.numeric(data2$`family or spouse not allow schooling`)

data2 = data2[c(-6,-12),]


data2 = data2 %>%
  mutate(type = case_when(rownames(data2) %in% c(1:5) ~ "total",
                            rownames(data2) %in% c(6:10) ~ "male",
                            TRUE ~ "female"))



i = 1
while (i <= 15 ) {
data2[c(i),1]   = "Primary school"
data2[c(i+1),1] = "Junior high school"
data2[c(i+2),1] = "High school"
data2[c(i+3),1] = "Vocational high school"
data2[c(i+4),1] = "Higher education"
i = i+5;
}


data2 = pivot_longer(data2,  "his/her education is enough":"other", names_to = "reason")
completed_education_level type reason value
Primary school total his/her education is enough 189
Primary school total failed exam 125
Primary school total not interested in school 199
Primary school total to cost of studying to high 491
Primary school total want to work 145
Primary school total family or spouse not allow schooling 421
pie_col = get_brewer_pal("Spectral", n = 9)

data2 %>% 
  filter(type == "total") %>%
  group_by(reason) %>%
  summarise(total_value=sum(value)) %>%
  ggplot(aes(x="", y=total_value, fill=reason)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0) +
  scale_fill_manual(values=c(pie_col)) +
  geom_text(aes(label = paste0(round(((total_value*100)/sum(total_value))), "%")), position = position_stack(vjust = 0.5))+
  labs(x = NULL, y = NULL, fill = NULL, title = "What Are The Reasons People \nDo Not Go To University?") +
  theme_classic() + 
  theme(axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(hjust = 0.5, color = "#666666"))

data2 %>%
  filter(completed_education_level == "Primary school" & type == "total") %>%
  ggplot(aes(x=value, y=reorder(reason, +value), fill = reason)) +
  geom_bar(stat="identity", position = "dodge") +
  scale_fill_manual(values=c(pie_col)) +
  labs(x="", y="",title = "What Is The Reason For Not Attending Education\nAfter Primary School",
       subtitle = "Total & 15-34 Age group & 2016 & Thousand person") +
  scale_x_continuous(expand=c(0,0),limits = c(0,530), n.breaks = 10 )  +
  scale_y_discrete(
            breaks = c("completed_education_level",
                     "Total",
                     "his/her education is enough",
                     "failed exam",
                     "not interested in school",
                     "to cost of studying to high",
                     "want to work",
                     "family or spouse not allow schooling",
                     "marriage or other family reason",
                     "disability and health reason",
                     "other")) + 
  theme_classic() +
  theme(legend.position = "none",
        axis.text.x = element_text(size=8,color="black"),
        axis.text.y =element_text(size=8,color="black"),
        plot.title=element_text(color="black",size=10, face = "bold"),
        plot.subtitle = element_text(color="black",size=10))

While the high cost of education at low education levels plays a major role in not continuing education, it is seen that this reason loses its importance and decreases as the education level rises. In addition, failing the exams stands out as the most important reason for not continuing education at high school and vocational high school level.

data2 %>%
  filter((type == "male" | type == "female") & completed_education_level == "Primary school")  %>%
  ggplot(aes(x=value, y=reorder(reason, +value), fill = type)) +
  geom_bar(stat="identity", position="dodge") +
  scale_fill_manual(values=c("male"="#355F3B", "female"="#FED700")) +
  labs(x="", y="",title = "Comparison of Not Attending Reasons To University \nBetween Man and Women After Primary School",
       subtitle = "15-34 Age group & 2016 & Thousand person") +
  scale_x_continuous(expand=c(0,0),limits = c(0,430), n.breaks = 10 )  +
  theme_classic() +
  theme(axis.text.x = element_text(size=8,color="black"),
        axis.text.y =element_text(size=8,color="black"),
        plot.title=element_text(color="black",size=10, face = "bold"),
        plot.subtitle = element_text(color="black",size=10),
        legend.title = element_blank())

While the number of women who did not complete the education process due to marriage and family problems in the early stages of education is quite high compared to men, the number of men who do not continue their education process due to their desire to work is also higher than women. Forced marriage of women at an early age due to family pressure and the desire of men to enter business life at an early age to help their families financially may be one of the factors in this situation.

Introduction of Third Education Statistic

Another aim of the project is to show how the education expenditures for various education levels and the amount of expenditure per student at each education level have changed over the years. Therefore, data containing information on education expenditure per student by education level between 2011 and 2020 were used.

data3 = read_excel("data/education_expenditure_per_student_by_education_level.xls", range = "A15:E69")


i = 1
while(i<=10){
 data3[i,1] = "pre-primary"
 data3[i+11,1] = "primary"
 data3[i+22,1] = "lower secondary"
 data3[i+33,1] = "upper secondary"
 data3[i+44,1] = "tertiary"
 i = i + 1
}

data3 = drop_na(data3);

data3 = data3 %>%
          setNames(c("education_level",
                     "year",
                     "expenditure",
                     "exp per student (TL)",
                     "exp per student ($)"))
education_level year expenditure exp per student (TL) exp per student ($)
pre-primary 2011 4126.437 3528 2103
pre-primary 2012 4972.221 4461 2477
pre-primary 2013 5312.991 4980 2614
pre-primary 2014 6587.123 5893 2689
pre-primary 2015 7221.838 6078 2231
pre-primary 2016 9034.809 7062 2333
data3 %>%
  ggplot(aes(x = expenditure/1000, y=education_level)) +
  geom_segment( aes(x=0, xend=expenditure/1000, y=education_level, yend=education_level), color="chartreuse4") +
  geom_point( color="chartreuse2", size=3, alpha=0.6) +
  scale_x_continuous(expand=c(0,0),limits = c(0,95),n.breaks = 10 )  +
  transition_time(year) +
  labs(x = "Expenditure", y = "",
       title ="How Do Expenditures on Education Levels Change \nBetween 2011-2020?",
       subtitle = "Year: {as.integer(frame_time)} & Billion TL") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.y =element_text(size=6,color="black"),
    axis.title.y=element_blank(),
    axis.text.x = element_text(size=6,color="black"),
    axis.title.x=element_text(size=6, hjust = 1),
    plot.title=element_text(color="black",size=6, face = "bold"),
    plot.subtitle = element_text(color="black",size=6)) 

data3 %>%
   ggplot(aes(x = `exp per student (TL)`/1000, y=education_level)) +
  geom_segment( aes(x=0, xend=`exp per student (TL)`/1000, y=education_level, yend=education_level), color="#ab0505") +
  geom_point( color="#ab0505", size=3, alpha=0.6) +
  scale_x_continuous(expand=c(0,0),limits = c(0,23), n.breaks = 11 )  +
  labs(x = "Expenditure per Student", y = "",
       title ="How Do Expenditures per Student on Education Levels \nChange Between 2011-2020?",
       subtitle = "Year: {as.integer(frame_time)} & Thousand TL") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.y =element_text(size=6,color="black"),
    axis.title.y=element_blank(),
    axis.text.x = element_text(size=6,color="black"),
    axis.title.x=element_text(size=6, hjust = 1),
    plot.title=element_text(color="black",size=6, face = "bold"),
    plot.subtitle = element_text(color="black",size=6)) +
  transition_time(year)

In general, spending on all levels of education is increasing over the years, and accordingly, spending per student at each level of education is also increasing. In addition, it is observed that the higher the education level, the higher the expenditures. Tertiary education is the education level with the highest expenditure per student.

Introduction of Last Education Statistic

The final aim of the project is to show what the educational status of individuals living in provinces in Turkey is and how it has changed over 4-year periods between 2008-2020.

data4 = read_excel("data/education_level_completed_by_province.xls", range = "A7:AW1072")


data4 = data4 %>% select_if(~sum(!is.na(.)) > 0)

data4 = drop_na(data4)

data4 = data4[-c(4:6)]

data4 = data4[-seq(5,32,by=3)]
data4 = data4[-seq(5,23,by=2)]

colnames(data4)[c(1,2,3,4,5,6)] = paste(c("Year",
                                       "Province_code",
                                       "Province_name",
                                       "Illiterate",
                                       "Literate_without_a_diploma",
                                       "Primary_school"),
                                     sep="")

colnames(data4)[c(7,8,9,10,11,12,13)] = paste(c("Primary_education",
                                             "Junior_and_vocational_high_school",
                                             "High_and_vocational_high_school",
                                             "Universities_and_other_higher_educational_institutions",
                                             "Master",
                                             "Doctorate",
                                             "Unknown"),
                                           sep="")

i = 4
for(i in 4:length(data4)){
  data4[[i]] = as.numeric(data4[[i]])
}


province_names = data4 %>%
                  filter(Year == 2008) %>%
                   select(Province_name) %>%
                    arrange(Province_name)
Year Province_code Province_name Illiterate Literate_without_a_diploma Primary_school Primary_education Junior_and_vocational_high_school High_and_vocational_high_school Universities_and_other_higher_educational_institutions Master Doctorate Unknown
2008 1 Adana 151423 88124 469036 164036 84299 300221 93801 4705 1505 104772
2008 2 Adıyaman 70507 33368 107482 57856 16956 62867 13952 506 103 28456
2008 3 Afyon 48353 30108 228018 60563 26293 82334 23466 1408 412 19716
2008 4 Ağrı 66651 57594 57624 28685 5175 27576 5404 416 111 63334
2008 5 Amasya 21599 18221 95430 27658 12249 42760 14956 494 103 18039
2008 6 Ankara 153179 118282 977183 328453 245614 930776 442315 44598 16239 251443
url = "https://tr.wikipedia.org/wiki/T%C3%BCrkiye%27deki_illerin_geli%C5%9Fmi%C5%9Flik_d%C3%BCzeyleri#%C4%B0nsani_Geli%C5%9Fme_Endeksi'ne_g%C3%B6re"


html = read_html(url)

development_index_of_cities = html %>% 
                                html_elements("table") %>%
                                  .[[1]] %>%
                                    html_table()

development_index_of_cities = development_index_of_cities %>%
                                select(-1)

development_index_of_cities$Kademe = as.factor(development_index_of_cities$Kademe)

colnames(development_index_of_cities)[c(1,2,3)] = paste(c("Province_name",
                                                          "Score",
                                                          "level"), sep = "")

development_index_of_cities = development_index_of_cities %>%
                                arrange(Province_name)


development_index_of_cities[,"Province_name"] = province_names$Province_name
Province_name Score level
Adana 0,353 3
Adıyaman -0,926 6
Afyon -0,023 4
Ağrı -1,752 6
Aksaray -0,271 4
Amasya 0,054 4
tur_polbnda_adm1_sf = st_read("./data/turkey_administrativelevels0_1_2/tur_polbnda_adm1.shp")
## Reading layer `tur_polbnda_adm1' from data source 
##   `C:\Users\turab\Documents\R\project_final_report-atakar\data\turkey_administrativelevels0_1_2\tur_polbnda_adm1.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 81 features and 8 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 25.66851 ymin: 35.80842 xmax: 44.81793 ymax: 42.10479
## Geodetic CRS:  WGS 84
tur_polbnda_adm1_sf = tur_polbnda_adm1_sf %>%
                          select(adm1_tr) %>%
                            rename("Province_name" = "adm1_tr")

tur_polbnda_adm1_sf[c(68,69,70,71,72),] = tur_polbnda_adm1_sf[c(69,70,72,68,71),]

tur_polbnda_adm1_sf[,"Province_name"] = province_names$Province_name 



tur_pntcntr_adm1_sf = st_read("./data/turkey_centralpoints_1_2/tur_pntcntr_adm1.shp")
## Reading layer `tur_pntcntr_adm1' from data source 
##   `C:\Users\turab\Documents\R\project_final_report-atakar\data\turkey_centralpoints_1_2\tur_pntcntr_adm1.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 81 features and 8 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 26.40629 ymin: 36.19888 xmax: 44.0455 ymax: 42.02687
## Geodetic CRS:  WGS 84
data4 %>%
  pivot_longer("Illiterate":"Unknown", names_to = "edu_lev", values_to = "value") %>%
  filter(edu_lev == "Illiterate") %>% 
  ggplot(aes(x = value/1000 , y=Province_name)) +
  geom_segment( aes(x=0, xend=value/1000, y=Province_name, yend=Province_name), color="#ab0505") +
  geom_point( color="#ab0505", alpha=0.6) +
  labs(x = "Number of Person", y = "",
       title ="Number of Illiterate People in Turkey by Year",
       subtitle = " Year: {as.integer(frame_time)} \nPopulation 15 years of age and over & Thousand Person") +
  scale_x_continuous(expand=c(0,0),limits = c(0,500), n.breaks = 20 )  +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.y =element_text(size=4,color="black"),
    axis.title.y=element_blank(),
    axis.text.x = element_text(size=4,color="black"),
    axis.title.x=element_text(size=6, hjust = 1),
    plot.title=element_text(color="black",size=6, face = "bold"),
    plot.subtitle = element_text(color="black",size=6))  +
    transition_time(Year)

As it can be understood from the point graph above, there is a decrease in the number of illiterate people in almost all provinces in Turkey from 2008 to 2020. It is seen that Istanbul experienced the biggest decrease in the number of illiterate people depending on the population. Adana, İzmir and Şanlıurfa are also the provinces that experienced a great decrease in the number of illiterate people after İstanbul.

data4_2008 = data4 %>%
  filter(Year == 2008) %>%
  select(-c(2,4,5)) %>%
  arrange(Province_name)

turkey_2008 = data4_2008 %>%
                full_join(development_index_of_cities) %>%
                  full_join(tur_polbnda_adm1_sf)

turkey_2008 <- st_as_sf(turkey_2008)

The development levels of the provinces in Turkey are reflected in the interactive maps below, thanks to the indexes determined according to the socio-economic development criteria of the Devlet Planlama Teşkilatı, and the educational status of the individuals living in that province is revealed above the provinces.

What is the educational status of the people in the provinces of Turkey in 2008?

turkey_2008 %>%
  leaflet() %>%
    addPolygons(fillColor = ~pal_col8(level),
                fillOpacity = 1,
                smoothFactor = 1,
                color = "black",
                weight = 1,
                label = labels8,
                labelOptions = labelOptions(style = list("color" = "white", #for popup label
                                                         "background-color" = "#57A0CE",
                                                         "border-color" = "#57A0CE",
                                                         "padding" = "20px"),
                                                  textsize = "15px",
                                                  direction = "auto"),
                highlight = highlightOptions(weight = 1,
                                             fillColor = "white",
                                             bringToFront = TRUE)) %>%
                addLabelOnlyMarkers(lng = tur_pntcntr_adm1_sf$longitude,
                                    lat = tur_pntcntr_adm1_sf$latitude,
                                    label = ~Province_name,
                                    labelOptions = labelOptions(noHide = T, 
                                                                direction = "center",
                                                                textsize = "9px",
                                                                style = list("color" = "white",
                                                                             "background-color" = "#1664AB",
                                                                             "border-color" = "#1664AB",
                                                                             "border-radius" = "35%",
                                                                             "padding" = "0px"))) %>%
  addLegend("bottomright", pal = pal_col8, values = ~level,
    title = "Dev. İndex (2017)",
    opacity = 1)
data4_2012 = data4 %>%
  filter(Year == 2012) %>%
  select(-c(2,4,5))%>%
  arrange(Province_name)


turkey_2012 = data4_2012 %>%
                full_join(development_index_of_cities) %>%
                  full_join(tur_polbnda_adm1_sf)


turkey_2012 <- st_as_sf(turkey_2012)

What is the educational status of the people in the provinces of Turkey in 2012?

data4_2016 = data4 %>%
  filter(Year == 2016) %>%
  select(-c(2,4,5))%>%
  arrange(Province_name)


turkey_2016 = data4_2016 %>%
                full_join(development_index_of_cities) %>%
                  full_join(tur_polbnda_adm1_sf)


turkey_2016 <- st_as_sf(turkey_2016)

What is the educational status of the people in the provinces of Turkey in 2016?

data4_2020 = data4 %>%
  filter(Year == 2020) %>%
  select(-c(2,4,5))%>%
  arrange(Province_name)


turkey_2020 = data4_2020 %>%
                full_join(development_index_of_cities) %>%
                  full_join(tur_polbnda_adm1_sf)


turkey_2020 <- st_as_sf(turkey_2020)

What is the educational status of the people in the provinces of Turkey in 2020?

Results and Discussion

As a result of the researches and comparisons, firstly, it was seen that there is a strong relationship between education levels and income. It is clearly seen that as the level of education completed increases, the earnings of individuals also increase. Also, being a man provides a privilege to earn more income compared to women, even if they have completed the same education level. Secondly, there are 3 main reasons why people interrupt their education life without going to university, namely the economic inadequacy of their families, unsuccessful results in exams and family problems. Therefore, Turkey should do its best to enable individuals to continue their education life and benefit from the best opportunities, and bring the economic level of each family to the level where they can at least meet the educational needs of their children. Third, it is seen that the education level with the highest expenditure is higher education. On the other hand, it has been observed that as the development levels of the provinces increase, the number of people who complete that education level also increases, regardless of their education level. Of course, in this case, the number of people living in those provinces also has an effect. In addition, it is seen that the number of illiterate people in Turkey has decreased considerably from 2008 to 2020.

Conclusion

As a result, some remarkable education statistics of Turkey are shown in this project. When we evaluate all the statistics as a whole, it would not be wrong to say that Turkey has made progress in the field of education. In particular, there has been a significant increase in the number of educated people over the years. If Turkey continues to invest more in the field of education, it will be inevitable to develop together with the educated individuals it has trained.

References

  1. Tüik

  2. https://www.displayr.com/how-to-make-a-pie-chart-in-

  3. https://tr.wikipedia.org/wiki/T%C3%BCrkiye%27deki_illerin_geli%C5%9Fmi%C5%9Flik_d%C3%BCzeyleri#%C4%B0nsani_Geli%C5%9Fme_Endeksi'ne_g%C3%B6re